A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.

Autor:	Rojas JC; Division of Pulmonology, Critical Care, and Sleep Medicine, Rush University, Chicago, IL., Lyons PG; Department of Medicine, Oregon Health & Science University, Portland, OR., Chhikara K; Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL., Chaudhari V; Division of Pulmonology, Critical Care, and Sleep Medicine, Rush University, Chicago, IL., Bhavani SV; Department of Medicine, Emory University, Atlanta, GA., Nour M; Department of Medicine, Emory University, Atlanta, GA., Buell KG; Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL., Smith KD; Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL., Gao CA; Division of Pulmonary and Critical Care, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL., Amagai S; Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL., Mao C; Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL., Luo Y; Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL., Barker AK; Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan, Ann Arbor, MI., Nuppnau M; Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan, Ann Arbor, MI., Beck H; MacLean Center for Clinical Medical Ethics, University of Chicago Medicine, Chicago, IL., Baccile R; Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL., Hermsen M; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI., Liao Z; Department of Medicine, University of Chicago, Chicago, IL., Park-Egan B; Department of Medicine, Oregon Health & Science University, Portland, OR., Carey KA; Department of Medicine, University of Chicago, Chicago, IL., XuanHan; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Tufts University School of Medicine, Boston, MA., Hochberg CH; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD., Ingraham NE; Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Minnesota Medical School; University of Minnesota, Minneapolis, MN., Parker WF; Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL.; MacLean Center for Clinical Medical Ethics, University of Chicago Medicine, Chicago, IL.; Department of Public Health Sciences, University of Chicago, Chicago, IL.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Sep 04. Date of Electronic Publication: 2024 Sep 04.
DOI:	10.1101/2024.09.04.24313058
Abstrakt:	Background: Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies. Methods: A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models. Results: We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality. Conclusions: CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.
Databáze:	MEDLINE
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